sslprovide (--f5 ssl) does not generate CLIENT/SERVER_TRAFFIC_SECRET on server-side TLS traffic
When I enable the sslprovider and start a tcpdump on the server-side in order to decode TLSv1.3 traffic, only the CLIENT_HANDSHAKE_TRAFFIC_SECRET and SERVER_HANDSHAKE_TRAFFIC_SECRET 'keys' are stored in the packet capture file, but the CLIENT_TRAFFIC_SECRET and SERVER_TRAFFIC_SECRET 'keys' are missing. This prevents me to decode the application data in the packet capture: # tmsh modify sys db tcpdump.sslprovider value enable # tcpdump -i <server-side-VLAN> -s0 -f5 ssl:v -vvv -w /var/tmp/output.cap <Generate traffic> # tshark -r /var/tmp/output.cap -Y "f5ethtrailer.tls.keylog" -T fields -e f5ethtrailer.tls.keylog On the client-side, this works as expected. Is this a bug (tested with TMOS 17.5.1)? Am I doing something wrong?AI Security - LLM-DOS, and predictions of 2025 and beyond
Introduction Hello again, this article is part of AI security series. I have been discussing AI security along with the OWASP LLM top10. LLM01 and LLM02 were discussed in the "AI Security : Prompt Injection and Insecure Output Handling", and LLM03 and its basic concepts were discussed in the "Using ChatGPT for security and introduction of AI security". In this article, I am going to discuss LLM04. And, since we are almost at the end of the year 2024, I would like to present some discussions and predictions for AI security in 2025 and beyond. LLM04: Model Denial of Service LLM04 is relatively easy to understand for security engineers who is familiar with conventional cyber attack methods. Denial of Service (DoS) is a common method of cyber attack, in which a large amount of data is given to the server to make it unable to provide services and/or crash. DoS attacks usually aim to exhaust computational resources and block services rather than stealing data, but the disruption they cause can be used as a smokescreen for more malicious activities, such as data breaches or malware installation. DOS attack against LLM (LLM-DOS) is same. It aims to exhaust computational resources of DOS (like CPU/GPU usage) and block services (like responding to chat). LLM-DOS can be done in two ways. One is a simple LLM-DOS attack which is to mass input against the LLM's input, similar to a DOS attack against a server. This method, as described in this article, can deplete the LLM's resources, like CPU/GPU usages. If you call this as a simple DoS attack, in such a scenario would be to instruct the model to keep repeating Hello, but we see that relying only on natural instructions limits the output length, which is limited by the maximum length of the LLM's Supervised Fine-Tuning (SFT) data The another method of LLM-DOS is to include code in the input that over-consumes resources. Denial-of-Service Poisoning Attacks on Large Language Models is discussing this. In the paper, this is called as a poisoning-based DoS (P-DoS) attack and it demonstrates that the output length limit can be broken by injecting a single poisoning sample designed for DoS purposes. Experiments reveal that an attacker can easily compromise models such as GPT-4o and GPT-4o mini by injecting a single poisoning sample through the OpenAI API at a minimal cost of less than $1. To understand this, it is easier to think about simple programming - for example, if you put an inescapable loop statement in your code, it can hang the computer (in fact, the IDE will warn you before it compiles). And if the network does not have a Spanning Tree Protocol, it will loop and hang the router. So same things happens on prompt injection. When using this idea LLLM-DOS, we must consider that such input should be blacklisted, so the simple way of using inescapable loop is impossible. Also, even if it is possible against WhiteBox, but we do not know what kind of attack is possible in BlackBox. However, according to "Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings", a Prompt input to the BlackBox can generate multiple sub-prompts (e.g., 25 sub-prompts). Its experiments show that the delay could be increased by a factor of 250. Given these serious safety concerns, researchers advocate further research aimed at defending against LLM-DoS threats in custom fine tuning of aligned LLMs. What will happen in 2025 and beyond? Some news site predicts an intensifying AI arms race in coming year. I would like to share an article on AI security predictions for the coming year and beyond. According to an article by EG Secure solutions, the generative AI makes it possible to create a malware without specialized skills, that makes easier to do cyber attacks. Thus, the article predicted that cyber attacks by malware created by generative AI would increase. The article also points out that LLM-generated applications such as RAGs are being used, but their code may contain vulnerabilities, and that will be another threat in 2025 and beyond. McAfee has released "McAfee Unveils 2025 Cybersecurity Predictions: AI-Powered Scams and Emerging Digital Threats Take Center Stage". According to the article, cyber attacks by malicious attackers will be highly optimized by generative AI, and the quality of DeepFake and AI-generated images/videos will increase, making it difficult to determine whether they are created by humans or generative AI. Thus it is expected that fake emails generated by generative AI, such as phishing emails, will also become harder to distinguish from real emails. Furthermore, the article points out that malware which is using (maybe created by) generative AI will become more sophisticated, thereby breaking through conventional security defense systems and may succeed to extracting personal information and sensitive data. Finally, the "Infosec experts divided on AI's potential to assist red teams" discusses the pros and cons of using generative AI for red teaming, one type of security audit. According to the article, the benefit of using generative AI is that it accelerates threat detection by allowing AI to scour multiple data feeds, applications, and other sources of performance data and run them as part of a larger automated workflow. On the other hand, the article also argues that using generative AI for red teaming is still limited, because the vulnerability discovery process by AI is a black box so the pen-tester cannot explain how they discovered to their clients.
Mitigating OWASP Web Application Risk: Insecure Design using F5 XC platform
Overview: This article is the last part in a series of articles on mitigation of OWASP Web Application vulnerabilities using F5 Distributed Cloud platform (F5 XC). Introduction to Insecure Design: In an effort to speed up the development cycle, some phases might be reduced in scope which leads to give chance for many vulnerabilities. To focus the risks which are been ignored from design to deployment phases, a new category of “Insecure Design” is added under OWASP Web Application Top 10 2021 list. Insecure Design represents the weaknesses i.e. lack of security controls which are been integrated to the website/application throughout the development cycle. If we do not have any security controls to defend the specific attacks, Insecure Design cannot be fixed by any perfect implementation while at the same time a secure design can still have an implementation flaw which leads to vulnerabilities that may be exploited. Hence the attackers will get vast scope to leverage the vulnerabilities created by the insecure design principles. Here are the multiple scenarios which comes under insecure design vulnerabilities. Credential Leak Authentication Bypass Injection vulnerabilities Scalper bots etc. In this article we will see how F5 XC platform helps to mitigate the scalper bot scenario. What is Scalper Bot: In the e-commerce industry, Scalping is a process which always leads to denial of inventory. Especially, online scalping uses bots nothing but the automated scripts which will check the product availability periodically (in seconds), add the items to the cart and checkout the products in bulk. Hence the genuine users will not get a fair chance to grab the deals or discounts given by the website or company. Alternatively, attackers use these scalper bots to abandon the items added to the cart later, causing losses to the business as well. Demonstration: In this demonstration, we are using an open-source application “Online Boutique” (refer boutique-repo) which will provide end to end online shopping cart facility. Legitimate customer can add any product of their choice to the cart and checkout the order. Customer Page: Scalper bot with automation script: The below automation script will add products in bulk into the cart of the e-commerce application and place the order successfully. import requests import random # List of User-Agents USER_AGENTS = [ "sqlmap/1.5.2", # Automated SQL injection tool "Nikto/2.1.6", # Nikto vulnerability scanner "nmap", # Network mapper used in reconnaissance "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)", # Spoofed Search Engine Bot "php", # PHP Command Line Tool "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1)", # Old Internet Explorer (suspicious outdated) "libwww-perl/6.36", # Perl-based automation, often found in attacks or scrapers "wget/1.20.3", # Automation tool for downloading files or making requests "Python-requests/2.26.0", # Python automation library ] # Function to select a random User-Agent def get_random_user_agent(): return random.choice(USER_AGENTS) # Base URL of the API BASE_URL = "https://insecure-design.f5-hyd-xcdemo.com" # Perform the API request to add products to the cart def add_to_cart(product_id, quantity): url = f"{BASE_URL}/cart" headers = { "User-Agent": get_random_user_agent(), # Random User-Agent "Content-Type": "application/x-www-form-urlencoded" } payload = { "product_id": product_id, "quantity": quantity } # Send POST request with cookies included response = requests.post(url, headers=headers, data=payload) if response.status_code == 200: print(f"Successfully added {quantity} to cart!") else: print(f"Failed to add to cart. Status Code: {response.status_code}, Response: {response.text}") return response # Perform the API request to place an order def place_order(): url = f"{BASE_URL}/cart/checkout" headers = { "User-Agent": get_random_user_agent(), # Random User-Agent "Content-Type": "application/x-www-form-urlencoded" } payload = { "email": "someone@example.com", "street_address": "1600 Amphitheatre Parkway", "zip_code": "94043", "city": "Mountain View", "state": "CA", "country": "United States", "credit_card_number": "4432801561520454", "credit_card_expiration_month": "1", "credit_card_expiration_year": "2026", "credit_card_cvv": "672" } # Send POST request with cookies included response = requests.post(url, headers=headers, data=payload) if response.status_code == 200: print("Order placed successfully!") else: print(f"Failed to place order. Status Code: {response.status_code}, Response: {response.text}") return response # Main function to execute the API requests def main(): # Add product to cart product_id = "OLJCESPC7Z" quantity = 10 print("Adding product to cart...") add_to_cart_response = add_to_cart(product_id, quantity) # If the add_to_cart request is successful, proceed to checkout if add_to_cart_response.status_code == 200: print("Placing order...") place_order() # Run the main function if __name__ == "__main__": main() To mitigate this problem, F5 XC is providing the feasibility of identifying and blocking these bots based on the configuration provided under HTTP load balancer. Here is the procedure to configure the bot defense with mitigation action ‘block’ in the load balancer and associate the backend application nothing but ‘evershop’ as the origin pool. Create origin pool Refer pool-creation for more info Create http load balancer (LB) and associate the above origin pool to it. Refer LB-creation for more info Configure bot defense on the load balancer and add the policy with mitigation action as ‘block’. Click on “Save and Exit” to save the Load Balancer configuration. Run the automation script by providing the LB domain details to exploit the items in the application. Validating the product availability for the genuine user manually. Monitor the logs through F5 XC, Navigate to WAAP --> Apps & APIs --> Security Dashboard, select your LB and click on ‘Security Event’ tab. The above screenshot gives the detailed info on the blocked attack along with the mitigation action. Conclusion: As you have seen from the demonstration, F5 Distributed Cloud WAAP (Web Application and API Protection) has detected the scalpers with the bot defense configuration applied on the Load balancer and mitigated the exploits of scalper bots. It also provides the mitigation action of “_allow_”, “_redirect_” along with “_block_”. Please refer link for more info. Reference links: OWASP Top 10 - 2021 Overview of OWASP Web Application Top 10 2021 F5 Distributed Cloud Services F5 Distributed Cloud Platform Authentication Bypass Injection vulnerabilitiesIdentity-centric F5 ADSP Integration Walkthrough
In this article we explore F5 ADSP from the Identity lense by using BIG-IP APM, BIG-IP SSLO and add BIG-IP AWAF to the service chain. The F5 ADSP addresses four core areas: Deployment at scale, Security against evolving threats, Deliver application reliably, Operate your day to day work efficiently. Each comes with its own challenges, but together they define the foundation for keeping systems fast, stable, and safe. Each architecture deployment example is designed to cover at least two of the four core areas: Deployment, Security, Delivery and XOps.
SSL cipher
Hi guys TLS is weird. Why is this behavior happening? The server that receives the client hello sends an alert. Transport Layer Security TLSv1.2 Record Layer: Handshake Protocol: Client Hello Content Type: Handshake (22) Version: TLS 1.0 (0x0301) Length: 688 Handshake Protocol: Client Hello Handshake Type: Client Hello (1) Length: 684 Version: TLS 1.2 (0x0303) The server only allows TLS 1.0. Our SSL profile is also set to only allow TLS 1.0.Runtime API Security: From Visibility to Enforced Protection
This article examines how runtime visibility and enforcement address API security failures that emerge only after deployment. It shows how observing live traffic enables evidence-based risk prioritization, exposes active exploitation, and supports targeted controls such as schema enforcement and scoped protection rules. The focus is on reducing real-world attack impact by aligning specifications, behavior, and enforcement, and integrating runtime protection as a feedback mechanism within a broader API security strategy.
getting compiling error when enabling Nginx App_potect
i m trying to install NGinx plus with App_ptotect but when trying to enable app_protect module after installing it i get the following error nginx: [emerg] APP_PROTECT config_set_id 1752649466-871-149162 not found within 45 seconds nginx: [emerg] APP_PROTECT fstat() "/opt/app_protect/config/compile_error_msg.json" failed (2: No such file or directory) and i can not start the nginx service, any idea about the issue?
